{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 什么是多项式回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.random.uniform(-3, 3, size=100)\n",
    "X = x.reshape(-1, 1)\n",
    "y = 0.5 * x**2 + x + 2 + np.random.normal(0, 1, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGTtJREFUeJzt3X+MZWV9x/HPl2XQWbQMDRPDDmyXP8jSKsZpJ9p0GyOg\nLqlWpvSHmGpqbbLxD6sQu3bRpmhbwzY0VtM0TTdKq8kGIUC3NNKCdTG2pFBm2aX83JZo+DGgrJVB\nwansj2//mHvZu3fPuffcc57z4znn/UoIO/fXee7cM9/7nO/zfZ7H3F0AgPidUncDAABhENABoCUI\n6ADQEgR0AGgJAjoAtAQBHQBagoAOAC1BQAeAliCgA0BLnFrlwc466yzftGlTlYcEgOjt27fv++4+\nO+5xlQb0TZs2aWlpqcpDAkD0zOyJLI8j5QIALUFAB4CWIKADQEsQ0AGgJQjoANASlVa5AEBX7Nm/\nrOvuOKhnVla1YWZa27du1uL8XKnHJKADQGB79i/r6lsf1Orho5Kk5ZVVXX3rg5JUalAn5QIAgV13\nx8FXgnnf6uGjuu6Og6Uel4AOAIE9s7KaePtyyu2hENABILANM9OJt5vW0jFlIaADQGDbt26WJdzu\nUqlpFwI6AAS2OD8nT7kvLR0TAgEdAEowl5J2SUvHhEBAB4AJ7Nm/rC079+q8HV/Tlp17U3Pi27du\n1vTUuhNum55ap+1bN5fWNurQASCjSerL+z9XObmIgA4AGY2qL1+cn0ucHXr3josrax8BHQAyShvQ\nfGZltbbZoYPG5tDN7Hoze87MHhq47afN7Otm9j+9/59ZbjMBoH5pA5obZqZrmx06KMug6N9LunTo\nth2SvuHu50v6Ru9nAGi1UQOdo3rvVRkb0N39W5J+MHTzZZK+3Pv3lyUtBm4XADTO4vycrr38Qs3N\nTMu0Vpp47eUXanF+bmTvvSp5c+ivc/dne//+rqTXpT3QzLZJ2iZJGzduzHk4AGiGxfm5xJz49q2b\nT8ihS+WXKQ4rXIfu7i6lToqSu+9y9wV3X5idnS16OABopFG996rk7aF/z8zOdvdnzexsSc+FbBQA\nxCit916VvAH9Nkm/I2ln7///GKxFAFCxOnYXKsPYgG5mN0h6m6SzzOxpSddoLZDfZGa/J+kJSb9V\nZiMBoCxNqB8PZWxAd/f3pdx1SeC2AEDlxs3+jAkzRQF0Wuj68TrTN6y2CKDTQtaP99M3yyurch1P\n35S5S9EgAjqATgu5zG3d0/9JuQDotJDL3NY9/Z+ADqDzQtWPb5iZ1nJC8K5q+j8pFwAIpI5digbR\nQweAQOrYpWgQAR0AAqpz+j8pFwBoCQI6ALQEAR0AWoKADgAtQUAHgJYgoANASxDQAaAlqEMHgAk0\neXcjAjoAZNT03Y1IuQBARnUvjzsOAR0AMqp7edxxCOgAkFHI3Y3KQEAHgIzqXh53HAZFASCjupfH\nHYeADgATqHN53HEI6AA6qcn15HkR0AF0TtPryfMqNChqZleZ2cNm9pCZ3WBmrw7VMAAoS9PryfPK\nHdDNbE7SRyUtuPsbJK2TdEWohgFAWZYbXk+eV9GUy6mSps3ssKT1kp4p3iQAKM+e/csySZ5wX5F6\n8ibk5HMHdHdfNrO/kPSkpFVJd7r7ncFaBgAj5A2g191xMDGYm5S7nrwpOfkiKZczJV0m6TxJGySd\nbmbvT3jcNjNbMrOlQ4cO5W8pAPT0A+jyyqpcxwPonv3LY5+bllZx5Q++TcnJFxkUfbuk77j7IXc/\nLOlWSb80/CB33+XuC+6+MDs7W+BwALCmSABNS6vMFUi3NGWNlyIB/UlJv2hm683MJF0i6dEwzQKA\ndEUCaBnT95uyxkvugO7u90q6WdL9kh7svdauQO0CgFRFAuji/JyuvfxCzc1My7TWM7/28gsL5bqb\nssaLuScND5RjYWHBl5aWKjsegHYaHoSU1gJo0cBctE1lVbmY2T53Xxj3OGaKAohOExfJasIaLwR0\nAFFqQgBtGgI6gM5pwiSgMhDQAXRKUyYBlYEdiwB0SlMmAZWBgA6gU5oyCagMBHQAndKUSUBlIKAD\n6JSmTAIqA4OiAFojS/VKE2vYQyGgA2iFtOqVpSd+oLseO3RS8G5DAB9GygVAK6RVr+y+58kTltm9\n6sYD+qM9D9bTyJIR0AG0wqh1zod/3n3Pk5nWTo8NAR1AK0xSpeJSK+rOhxHQAbRCUvWKjXh8G+rO\nhzEoCiCoutZJSapeueiCWe2+58ngG0I3FQEdQDB1r5OSVr0yHNTbUnc+jJQLgGCauE7Kny1eqL98\n75uC7lDUVPTQARTWT7MsN3SdlLbWnQ8joAMoJGk7uGFl5KvbuqZ5EQR0AIUkpVkGlZGvrjtX31Tk\n0AEUMiqdUla+uom5+iaghw6gkA0z04m587mZad294+JSjpllTfMupmTooQMopI7laMetad5PyQyu\n4XL1rQ+2crr/IAI6gEIW5+d07eUXVloWOO5LpKspGVIuAAqruixw3Jrmbd5mbhQCOoAoJOXE+zn6\n/n1X3XhAG2amNbN+Ss//+PBJr9HG6f6DCgV0M5uR9EVJb9DaAmYfcvf/CNEwAOgbVaYo6aT7pk4x\nTa0zHT56fMJ/W6f7DyraQ/+CpH9x998ws9MkrQ/QJgA4wbic+PB9h4+5ZqandPqrTu1UlUvugG5m\nZ0h6q6QPSpK7vyzp5TDNAoDj8uTEX1g9rAPXvLOsJjVSkSqX8yQdkvR3ZrbfzL5oZqcPP8jMtpnZ\nkpktHTp0qMDhAHTVqDLFcSWMXVIkoJ8q6ecl/Y27z0t6SdKO4Qe5+y53X3D3hdnZ2QKHA9BVo8oU\n66iDb6oiOfSnJT3t7vf2fr5ZCQEdAIoaV6Y47r6uyB3Q3f27ZvaUmW1294OSLpH0SLimAeiKLNP0\nR9W6d2V53HGKVrn8vqTdvQqXb0v63eJNAtAlrJwYTqGp/+5+oJcff6O7L7r786EaBqAbujpNvwys\n5QKgVl2dpl8GAjqAWlF2GA4BHUCp9uxf1pade3Xejq9py869Jy1hS9lhOCzOBaA0WQY8s5QkIhsC\nOoDSjBrwHAzYlB2GQUAHEERSLTkDntUioAMoLC210tV1yevCoCiAwtJSK+5iwLNCBHQAhaWlUF5Y\nPVz5fqNdFkXKJcs6DwDqs2FmWssJQX3DzDQDnhVqfA+9n5tbXlmV63hubriWFUB9qCVvhsb30LOW\nPY1CDx9dUde5Ti15MzQ+oBcte2IlN3RF3ec6qZX6NT7lUnSdB1ZyQ1d09Vwft7RAlzQ+oBfNzTGx\nAV3RxXOdMbYTNT6gL87PFSp7YiU3dEUXz/WuXpWkaXwOXSqWm9u+dfMJeUWJ0Xe0UxfP9S5elYwS\nRUAvgtF3dEUXz/VR9e9dZO5e2cEWFhZ8aWmpsuMBqF+ZpZTDlT3S2lVJ22ajmtk+d18Y97jW99CB\nrmjifIuySym7eFUyCgEdaIG6a9DThJgYOA7178c1vsoFwHhNrfZg0LJaBHSgBdIC5PLKaq012V0s\npawTAR1IENvsw1EBss6JNizaVS0COjAkxtmHSYGzr87US9GJgZhM4UFRM1snaUnSsru/u3iTgHpV\nMZAXWr9dV954IPH+OnPWDFpWJ0QP/WOSHg3wOkAjxDqQtzg/pzly1p1WKKCb2TmS3iXpi2GaA9Qv\n5oE8ctbdVrSH/nlJn5B0LEBbCottIAvNFHNQJGfdbblz6Gb2bknPufs+M3vbiMdtk7RNkjZu3Jj3\ncGM1dWIF4hP77ENy1t2Vey0XM7tW0gckHZH0akk/JelWd39/2nPKXMtly869iYv0zM1M6+4dF5dy\nTKArmrisQJdkXcsld8rF3a9293PcfZOkKyTtHRXMyxbrQBbQdDGWcXZVa+rQYx7IApqsqcsK4GRB\nFudy929K+maI18orbXH/iy6Y1Zade7lURCPFkMrg6jcerVltMWkg66ILZnXLvmUGStFIIQbyi34h\nZHk+m0jEo9UbXDBQiiYren4mbe5gkrz3GuOCe9bNIbqyiUSTscGFuFRsoxhSFFkVPT+Tctv97lmW\n3n7WJQ5iL+PsklYHdC4V2yVPiqLJXwBFz89xgX/c+jOTfKFQ2x6H1lS5JIl5xh9ONmm1RdPL7S66\nYHai24dlCfz94Jw0i5rKsPZpdUBnGnS7jOpRJgWsusvtxi1FcddjhxKfd8O9T2VavmLUkrl9G2am\nU7/YLrpglg5Py7R6UBTtkjaIODM9pZ8cOXbSoN1wMO8zSd/Z+a6ymikp20DieTu+pnF/feMGH/tf\nXMsrq68MiA4+99d/YU433PuUjib8nfcHTpuaksJxDIqiddLmGpgpsSe+ziwxkFWRUsgy4JiWQx/1\nnGGDue3h8YJ+2W7S70Bau7KpMzfe5PGNWEUd0DkhuiWt2uKqlE0djrqf1FMPmVIYdf5lGXBM+oIa\n95xRhoPzlp17R752nblyFtMrR7QBnROim5J6lP2Uw7AyUwrjzr8sFSzDX1CnBL6iGPVFUHeuPMZd\noWIQbUBPOyE+ftMDksIF9bxXAVw9VCctFdP/nZfxex8XkEa1adBwyiTLc7JK+1JZZ1Z7cQBzRMoR\nbUBP++CPugfrqee9CuDqoVp1THwZF5DytCnk+9izf1nPv/STxPve95Zzaz8PmSNSjmgD+qgBpVCX\nbnkvC7mcrF7Vg3tZUyqTtmnUc7Je9R3vUCRvJJZWLlmlrFcwmEy0dejjanBDXLrlvSwscjnJNnpx\nqHrS2iSTpJI6FIOakNZgjkg5ou2h9z/4j9/0QGmlaXkvC/M+j1RNPKpO80xy1TcuYDclrcFyAuFF\nG9Cl439UZV265b0szPu8rqdqYhtIrjIgTXLVNyodSVqj3aIO6FK5PaW8r533eV0e+efq5GSDX3CT\nlDSm1bfPTE/p0+95fWd/n10QfUCXwvWU0nqIeV47z/PSelYz66cmPn5sYr06KeuqYvgLLimYp/W2\nWe62u1oR0ENoQg9x+9bN2n7zAzp89MQ/3hf/74j27F9u9R9kjFcnZZ4zaQOb68x0zD0xSMeWskJ4\n0Va5hFb3ynzSWhA4/bSTv2MPH/PWb8gb41KuZZ4zaV9kx9z1nZ3v0t07Lk7cVaipSwWjGgT0nqb0\nEF9YPZx4+/LKatAyxqaVR+YtA6zzfZR5zkz6BdeEDgnqR0DvaUoPcdTxQvW6mtiby1OXXPf7KPOc\nmfQLrikdEtSLgN7TlN2Nxk2YCtHrKtKbK7NHvDg/p7t3XJyYUkhSZ690z/5lvfSTIyfdHuqcmfQL\nrikdEtSLQdGeplQGDLYjrZa4aK8rb2+uCQPHg+rqlSYtoiVJZ66f0jW/Gq4scJJKKabSQyKgn6Ap\nM9f67UjboadoryvvTNamlRbWtcBTWgXK+tNOre38aUqHBPUioGdUR0lYnl5Xlnbm7c1l7RFnaUOI\n32ddvdKm5qub0iFBfXIHdDM7V9JXJL1Oa1sZ7nL3L4RqWN0GA84Z01N66eUjr9SHL6+savvND+jT\ntz2sF1YPlxbgJ+11ZU2J5O3NZekRZ2lDqNRNXb1Sln5FU+XeJNrMzpZ0trvfb2avlbRP0qK7P5L2\nnFg2iU7LkY4ybjPfKqSlaOZmpnX3josLv36WjY+ztKGsdlZ1FTXu98AEH4RW+ibR7v6spGd7//6R\nmT0qaU5SakCPxbjlR5M0YZp62amALD3iLG0oo50hB2zHBeRRv4emDRyjW4Lk0M1sk6R5SfeGeL26\n5Q0sdedQq0gFDAezfolg//YsbSijnaEGbCdJWyW9btMGjtNwFdFOhevQzew1km6RdKW7/zDh/m1m\ntmRmS4cO1b9TShZ5A0vdOdQqaunHTebJ0oYy2hmq11+0tr2pA6aD6p6QhfIUCuhmNqW1YL7b3W9N\neoy773L3BXdfmJ2dLXK4yiQFnKlTTGeun5Jprd546hQ74f4m1PxWsQvMuICXpQ1ltDPUxJq0wNtf\nemHchKoYJviwTEB7FalyMUlfkvSou38uXJPqlyVX3NRL1rJL17L0QLO0IXQ7Q5UwpqWDTHrl9lF5\n8Rgm+MRwFYF8iuTQt0j6gKQHzexA77ZPuvvtxZtVv3EBJ0RAauqXwihNLdkLVcKYFJBNa3W5g1YP\nH9Vn/unhk14/hgk+Tf0MUVzussU8YilbrEKWEsAmirXdkxj+ok1bgkGSPv/eN0X3vrvwGbZN1rJF\nAnpNyq4ZH6fI1UGMVxZFpH1WUnWfV2hd+wxjV3odeheF/COoM49ZtFa6a1PMt2/drCtvPJB4X6x5\n5659hl3B8rkZhS71qrMagiqHySzOz2lmOnlfV/LOaBICekahg2Cd669T5TC5T7/n9Y1YLx8YhZRL\nRqGDYJ3VEDPrp/T8j0/e6m5mfXIvtA2KpstiqF4BCOgZlVHqNZzH7O8GVHbASBsHr3B8vFIhV3ck\ngKPJSLlkVHaKpMrp2GkbUafdHjvGDNAV9NAzKvuSe9yiTiErbPJcbcRc5saYAbqCgD6BMi+5RwWd\n0EuyTjo9PfYlYZkZia4g5dIQo8oY03rvH7/pgbGLRSWZdHGs2FMWdVYUAVWih16RcSmLUb3mq1Im\ntRz141viTdpjnuRqI/aUBRUq6Ap66BXIMuA5qtecJTVQZo85hiVhAdBDr0TWXWzSes1JvfckZfWY\nY1gSdpTYxwCArAjoFSiashhOGZxi9kq6ZVCIHvOo1FCsKYtxYwCxvi9gGAG9AmdMT2klocb7jJT1\nQZIM9t7Tlj8t2mMe15MdPP51dxzUVTceiCIIjtqFiJ472oQcegXMJrt9nLK2mstSzRLjfpRpVy7r\nzKKu3gGG0UOvwErCuimjbs+ijJr4LKmhWHa1H5Q2BpA2JhFL9Q4wjB56BWKpEsnSzhhLGNOuaOYi\n+VyArOihVyCWKpEs7Yx11mXaFU0MnwuQFT30CpSV8w4tSzuTZl2a1nLpk85YrVssnwuQFXuKYmL9\nKpfllVWZpMEziM2GgfCy7ilKD72j+muv510L5u4dF2tuZlrD3QGqRID6kEPvoFAzJ2McIAXajB56\nB4VaPXHS6p0iVwUAxiOgd1ConvUky9LGOCEJiE2hgG5ml5rZQTN73Mx2hGoUyhWqLn6SKpHY11QH\nYpA7h25m6yT9taR3SHpa0n1mdpu7PxKqcShHyLr4rDNWybcD5SvSQ3+zpMfd/dvu/rKkr0q6LEyz\nUKY66q9jmS0LxKxIlcucpKcGfn5a0luKNQdVKXN/1CSxzJYFYlZ62aKZbZO0TZI2btxY9uHQULGv\nqQ7EoEhAX5Z07sDP5/RuO4G775K0S1qbKVrgeIhc1VcFQNcUyaHfJ+l8MzvPzE6TdIWk28I0CwAw\nqdw9dHc/YmYfkXSHpHWSrnf3h4O1DAAwkUI5dHe/XdLtgdoCACiAmaIA0BIEdABoCQI6ALQEAR0A\nWoKADgAtwQYXKEV/mzpmhQLVIaAjuFA7IgGYDCkXBMfa50A9COgIjrXPgXoQ0BEca58D9SCgI7hJ\n9hoFEA6DogiOtc+BehDQUQrWPgeqR8oFAFqCgA4ALUFAB4CWIKADQEsQ0AGgJczdqzuY2SFJT+R8\n+lmSvh+wOXXivTRPW96HxHtpoqLv42fcfXbcgyoN6EWY2ZK7L9TdjhB4L83Tlvch8V6aqKr3QcoF\nAFqCgA4ALRFTQN9VdwMC4r00T1veh8R7aaJK3kc0OXQAwGgx9dABACNEFdDN7E/N7L/M7ICZ3Wlm\nG+puU15mdp2ZPdZ7P/9gZjN1tykPM/tNM3vYzI6ZWZTVCGZ2qZkdNLPHzWxH3e3Jy8yuN7PnzOyh\nuttShJmda2Z3mdkjvXPrY3W3KS8ze7WZ/aeZPdB7L58p9XgxpVzM7Kfc/Ye9f39U0s+5+4drblYu\nZvZOSXvd/YiZ/bkkufsf1tysiZnZz0o6JulvJf2Buy/V3KSJmNk6Sf8t6R2SnpZ0n6T3ufsjtTYs\nBzN7q6QXJX3F3d9Qd3vyMrOzJZ3t7veb2Wsl7ZO0GOlnYpJOd/cXzWxK0r9L+pi731PG8aLqofeD\nec/pkuL5Nhri7ne6+5Hej/dIOqfO9uTl7o+6e8ybhb5Z0uPu/m13f1nSVyVdVnObcnH3b0n6Qd3t\nKMrdn3X3+3v//pGkRyVFuRazr3mx9+NU77/S4lZUAV2SzOyzZvaUpN+W9Md1tyeQD0n657ob0VFz\nkp4a+PlpRRo82sjMNkmal3RvvS3Jz8zWmdkBSc9J+rq7l/ZeGhfQzexfzeyhhP8ukyR3/5S7nytp\nt6SP1Nva0ca9l95jPiXpiNbeTyNleR9AaGb2Gkm3SLpy6Oo8Ku5+1N3fpLWr8DebWWnpsMbtWOTu\nb8/40N2Sbpd0TYnNKWTcezGzD0p6t6RLvMGDGRN8JjFalnTuwM/n9G5DjXr55lsk7Xb3W+tuTwju\nvmJmd0m6VFIpA9eN66GPYmbnD/x4maTH6mpLUWZ2qaRPSHqPu/+47vZ02H2Szjez88zsNElXSLqt\n5jZ1Wm8g8UuSHnX3z9XdniLMbLZfwWZm01obfC8tbsVW5XKLpM1aq6p4QtKH3T3K3pSZPS7pVZL+\nt3fTPTFW7JjZr0n6K0mzklYkHXD3rfW2ajJm9iuSPi9pnaTr3f2zNTcpFzO7QdLbtLay3/ckXePu\nX6q1UTmY2S9L+jdJD2rtb12SPunut9fXqnzM7I2Svqy1c+sUSTe5+5+UdryYAjoAIF1UKRcAQDoC\nOgC0BAEdAFqCgA4ALUFAB4CWIKADQEsQ0AGgJQjoANAS/w9OM8sMLR1vkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110da9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性回归？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)\n",
    "y_predict = lin_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164ffc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(x, y_predict, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解决方案， 添加一个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X2 = np.hstack([X, X**2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lin_reg2 = LinearRegression()\n",
    "lin_reg2.fit(X2, y)\n",
    "y_predict2 = lin_reg2.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDKGALiKSIRTQRUQyhAK6iEiG+H86EuxDNa68cwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168dae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(np.sort(x), y_predict2[np.argsort(x)], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.99870163,  0.54939125])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8855236786516001"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg2.intercept_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
